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Prediction of drug combinations by integrating molecular and pharmacological data

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Item Type:Article
Title:Prediction of drug combinations by integrating molecular and pharmacological data
Creators Name:Zhao, X.M. and Iskar, M. and Zeller, G. and Kuhn, M. and van Noort, V. and Bork, P.
Abstract:Combinatorial therapy is a promising strategy for combating complex disorders due to improved efficacy and reduced side effects. However, screening new drug combinations exhaustively is impractical considering all possible combinations between drugs. Here, we present a novel computational approach to predict drug combinations by integrating molecular and pharmacological data. Specifically, drugs are represented by a set of their properties, such as their targets or indications. By integrating several of these features, we show that feature patterns enriched in approved drug combinations are not only predictive for new drug combinations but also provide insights into mechanisms underlying combinatorial therapy. Further analysis confirmed that among our top ranked predictions of effective combinations, 69% are supported by literature, while the others represent novel potential drug combinations. We believe that our proposed approach can help to limit the search space of drug combinations and provide a new way to effectively utilize existing drugs for new purposes.
Keywords:Computational Biology, Computers, Drug Combinations, Drug Design, Drug Therapy, Pharmaceutical Technology, Probability, Software, Statistical Models, Theoretical Models
Source:PLoS Computational Biology
ISSN:1553-734X
Publisher:Public Library of Science (U.S.A.)
Volume:7
Number:12
Page Range:e1002323
Date:29 December 2011
Official Publication:https://doi.org/10.1371/journal.pcbi.1002323
PubMed:View item in PubMed

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